Five Questions to Help CIOs Avoid IoT Data Problems

Enterprises will account for 46 percent of Internet of Things (IoT) device shipments this year, BI Intelligence predicts. That’s not surprising when you consider the incredible predictions around IoT savings (billions, according to Business Insider) and IoT revenues ($14.4 trillion by 2022, according to this Forbes column).

“With a sensor on absolutely everything – from cars and houses to your family members – it goes without saying there will be some challenges with these massive amounts of data,” Wood writes. “After all, IoT isn’t just about connecting things to the Internet; it’s about generating meaningful data.”

For enterprises, this will require a serious shift in the supporting infrastructure for data and analytics. Here are five data-related issues you’ll need to consider:

1. What do you mean by real-time data? Real-time is a bit of a misnomer, since it can be measured in minutes, seconds or milliseconds. “It may be noteworthy to point out that ‘real-time’ means something different in every application,” Wood states, noting that emergency situations will require less than a millisecond, but monitoring tanks might entail a longer lag of seconds. “The level of urgency will determine how much the application developer will invest in high-speed technology (whether it be cellular 2G vs. LTE or Bluetooth vs. WiFi).”

2. How will you adapt data analytics to the demands of IoT? As this QUIRK’s article eloquent points out, sensor data adds up quickly. “The drips of data they emit will aggregate into a raging torrent of information that must be stored and analyzed, sometimes in near real-time,” the article states. “That’s going to require staggering amounts of processing power and a lot of code.”

Typically, IoT use cases require real-time analytics, Wood writes. Typically, you can achieve this in one of two ways: Run some processing on the IoT device itself so that only exceptions are sent to the network or send the data to the cloud for processing, she adds.

3. What are your storage needs for IoT data? Data governance rules become particularly important with IoT data, with the chief issue being what is stored and for how long. While most use cases involve an immediate response — e.g., when a sensor detects a water leak — the bigger value may be in analyzing historical data.

4. What are your network needs for the IoT? Most data center WAN links are designed for humans interacting with applications, Gartner notes. That’s a drop in the bucket for IoT devices, which constantly send data. One device may not challenge your network, but what about 10? 20? 100? Thousands? To avoid bottlenecks, you’ll need to rethink inbound bandwidth.

“To truly realize the value of the IoT, they will need to make the data being collected accessible and useful,” Kabra writes. “Because of this, APIs are starting to move out from the shadows of the back office and into the forefront of an enterprise’s digital strategy.”

Loraine Lawson is a veteran technology reporter and blogger. She currently writes the Integration blog for IT Business Edge, which covers all aspects of integration technology, including data governance and best practices. She has also covered IT/Business Alignment and IT Security for IT Business Edge. Before becoming a freelance writer, Lawson worked at TechRepublic as a site editor and writer, covering mobile, IT management, IT security and other technology trends. Previously, she was a webmaster at the Kentucky Transportation Cabinet and a newspaper journalist. Follow Lawson at Google+ and on Twitter.

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